Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes

Hong, Libin (2018) Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes. PhD thesis, University of Nottingham.

[img] PDF (Thesis - as examined) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (6MB)

Abstract

A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances.

Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Cartlidge, John
Özcan, Ender
Bai, Ruibin
Keywords: hyper-heuristic; evolutionary programming;
Subjects: Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools: UNNC Ningbo, China Campus > Faculty of Science and Engineering > School of Computer Science
Item ID: 52348
Depositing User: HONG, Libin
Date Deposited: 13 Aug 2018 08:45
Last Modified: 07 May 2020 17:47
URI: http://eprints.nottingham.ac.uk/id/eprint/52348

Actions (Archive Staff Only)

Edit View Edit View